Deep Learning · TEE · guide

Neural-network foundations

  1. What does learning from data mean
  2. Your first complete training step
  3. Classification regression and learning types
  4. Training validation and test data
  5. Parameters and hyperparameters
  6. Artificial intelligence machine learning and deep learning
  7. What one artificial neuron does
  8. Model answer: explain an artificial neuron
  9. Perceptrons and the threshold
  10. Why XOR needs more than one perceptron
  11. Activation functions and how to calculate them
  12. Model answer: why a network needs nonlinear activation
  13. Choosing a loss
  14. Forward propagation through multiple neurons
  15. Backpropagation with one hidden neuron
  16. More outputs and vectorized backpropagation
  17. Model answer: forward propagation, backpropagation and updating
  18. Counting parameters in a dense network
  19. What you should understand before optimization
  20. Complete sigmoid training example
  21. Cross-entropy for probability distributions
  22. Biological neurons and the McCulloch-Pitts model